code: def _get_custom_behaviour(estimator_cls):
      train_size = 100 * 0.75  # cf. _get_data
      custom = dict(
          H2OAggregatorEstimator=dict(
              predict=False, result_shape=(train_size, 5 + 1)
          ),
          H2OGeneralizedLowRankEstimator=dict(
              results_may_differ=True,
              idempotent=False,
              result_shape=(train_size, 5),
          ),  # (n_samples, n_features)
          H2OPrincipalComponentAnalysisEstimator=dict(
              result_shape=(train_size, 2)
          ),  # (n_samples, k)
          H2OSingularValueDecompositionEstimator=dict(
              result_shape=(train_size, 2)
          ),  # (n_samples, nv)
          H2OTargetEncoderEstimator=dict(
              result_shape=(train_size, 5 + 2)
          ),
      )
      return custom.get(estimator_cls.__name__, dict())
  
file_path: h2o-3/h2o-py/tests/testdir_sklearn/pyunit_sklearn_estimators_as_transformers.py
imports: from collections import defaultdict
  from functools import partial
  import gc, inspect, os, sys
  import numpy
  from sklearn.datasets import make_classification, make_regression
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  import h2o
  from h2o.sklearn.wrapper import H2OConnectionMonitorMixin
  from tests import pyunit_utils, Namespace
label_desc: Use image node_id 3 for calling a global function with example usage: _get_custom_behaviour(estimator_cls) and returns: custom